Preadmission predictors of graduation success from a physical therapy education program in the United States
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PurposeThe field of physical therapy education is seeking an evidence-based approach for admitting qualified applicants, as previous research has assessed various outcomes, impeding practical application. This study was conducted to identify preadmission criteria predictive of graduation success. MethodsData from the 2013-2016 graduating cohorts (n=149) were collected. Predictors included verbal Graduate Record Examination rank percentile (VGRE%), quantitative GRE rank percentile, analytical GRE rank percentile, the admissions interview, precumulative science grade point average (SGPA), precumulative grade point average (UGPA), and a reflective essay. The National Physical Therapy Examination (NPTE) and grade point average at the time of graduation (GGPA) were used as measures of graduation success. Two separate mixed-effects models determined the associations of preadmission predictors with NPTE performance and GGPA. ResultsThe NPTE model fit comparison showed significant results (degrees of freedom [df]=10, P=0.001), decreasing within-cohort variance by 59.5%. NPTE performance was associated with GGPA (β=125.21, P=0.001), and VGRE%, the interview, the essay, and GGPA (P≤0.001) impacted the model fit. The GGPA model fit comparison did not show significant results (df=8, P=0.56), decreasing within-cohort variance by 16.4%. The GGPA was associated with the interview (β=0.02, P=0.04) and UGPA (β=0.25, P=0.04), and VGRE%, the interview, UGPA, and the essay (P≤0.02) impacted model fit. ConclusionIn our findings, GGPA predicted NPTE performance, and the interview and UGPA predicted GGPA. Unlike past evidence, SGPA showed no predictive power. The essay and VGRE% warrant attention because of their influence on model fit. We recommend that admissions ranking matrices place a greater weight on the interview, UGPA, VGRE%, and the essay.
Abstract
PurposeThe field of physical therapy education is seeking an evidence-based approach for admitting qualified applicants, as previous research has assessed various outcomes, impeding practical application. This study was conducted to identify preadmission criteria predictive of graduation success. MethodsData from the 2013-2016 graduating cohorts (n=149) were collected. Predictors included verbal Graduate Record Examination rank percentile (VGRE%), quantitative GRE rank percentile, analytical GRE rank percentile, the admissions interview, precumulative science grade point average (SGPA), precumulative grade point average (UGPA), and a reflective essay. The National Physical Therapy Examination (NPTE) and grade point average at the time of graduation (GGPA) were used as measures of graduation success. Two separate mixed-effects models determined the associations of preadmission predictors with NPTE performance and GGPA. ResultsThe NPTE model fit comparison showed significant results (degrees of freedom [df]=10, P=0.001), decreasing within-cohort variance by 59.5%. NPTE performance was associated with GGPA (β=125.21, P=0.001), and VGRE%, the interview, the essay, and GGPA (P≤0.001) impacted the model fit. The GGPA model fit comparison did not show significant results (df=8, P=0.56), decreasing within-cohort variance by 16.4%. The GGPA was associated with the interview (β=0.02, P=0.04) and UGPA (β=0.25, P=0.04), and VGRE%, the interview, UGPA, and the essay (P≤0.02) impacted model fit. ConclusionIn our findings, GGPA predicted NPTE performance, and the interview and UGPA predicted GGPA. Unlike past evidence, SGPA showed no predictive power. The essay and VGRE% warrant attention because of their influence on model fit. We recommend that admissions ranking matrices place a greater weight on the interview, UGPA, VGRE%, and the essay.
참고문헌 (11)
- Ruscingno G, "Admission variables and academic success in the first year of the professional phase in a doctor of physical therapy program", J Allied Health, 39, pp.138~142 (2010)
- Matthew Nuciforo, "Variables Predictive of Admission to US Physical Therapist Education Programs", Journal of Physical Therapy Education, 28(3), pp.112~119 (2014), Ovid Technologies (Wolters Kluwer Health)[DOI]
- Nancy Fell, "The Preprofessional Degree: Is It a Predictor of Success in Physical Therapy Education Programs?", Journal of Physical Therapy Education, 29(3), pp.13~21 (2015), Ovid Technologies (Wolters Kluwer Health)[DOI]
- Phillip Eugene Jones, "Imperfect physician assistant and physical therapist admissions process in United States of America", Journal of Educational Evaluation for Health Professions, 11, pp.11~ (2014), Korea Health Personnel Licensing Examination Institute[DOI]
- Eric B. Hekler, "Are daily fluctuations in perceived environment associated with walking?", Psychology & Health, 27(9), pp.1009~1020 (2012), Informa UK Limited[DOI]
- Quinn Capers, "Implicit Racial Bias in Medical School Admissions", Academic Medicine, 92(3), pp.365~369 (2017), Ovid Technologies (Wolters Kluwer Health)[DOI]
- "PTCAS application instructions" (2017)[URL]
- Liane Moneta-Koehler, "The Limitations of the GRE in Predicting Success in Biomedical Graduate School", PLOS ONE, 12(1), pp.e0166742~ (2017), Public Library of Science (PLoS)[DOI]
- Diane U. Jette, "Unflattening", Journal of Physical Therapy Education, 30(3), pp.4~10 (2016), Ovid Technologies (Wolters Kluwer Health)[DOI]
- Chad Cook, "20th Pauline Cerasoli Lecture: The Sunk Cost Fallacy", Journal of Physical Therapy Education, 31(3), pp.10~14 (2017), Ovid Technologies (Wolters Kluwer Health)[DOI]
- Denise Wise, "Defining Underrepresented Minorities and Promoting Holistic Review Admission Strategies in Physical Therapist Education", Journal of Physical Therapy Education, 31(4), pp.8~13 (2017), Ovid Technologies (Wolters Kluwer Health)[DOI]